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| Section | Weight | Objectives |
|---|---|---|
| Data Research and Analysis | 15% | - Data collection, preparation and preprocessing - Data quality, bias and validation - Data visualization and insight generation |
| Agentic AI | 15% | - Model Context Protocol (MCP) framework - Human-in-the-loop strategies - Agent design principles and capabilities - Agentic AI vs Generative AI use cases |
| AI Ethics and Security | 20% | - Responsible AI principles - AI-specific threats, risks and misinformation - Compliance and governance frameworks - Data privacy and security in AI systems |
| AI for Code and Workflow Optimization | 15% | - AI-assisted code generation and improvement - Integration of AI tools in development processes - Automation and workflow enhancement |
| Prompt Engineering | 15% | - Defensive prompting and error mitigation - Prompt design principles and best practices - Advanced prompting techniques |
| Generative AI Models | 20% | - Generative AI model families and use cases - Model selection and evaluation in AI hubs - Context windows, token limits and response management - Model hosting options and trade-offs |
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NEW QUESTION # 70
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?
Answer: D
Explanation:
Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.
NEW QUESTION # 71
Why is a Human-in-the-Loop workflow essential when an AI agent has write access to a production database?
Answer: D
Explanation:
A Human-in-the-Loop workflow adds human approval before the agent performs high-risk write operations, helping prevent accidental, incorrect, or harmful changes to production data.
NEW QUESTION # 72
Which role does AI play in assisting with the refactoring of software code?
Answer: A
Explanation:
AI-assisted refactoring helps analyze existing code to identify design flaws, simplify structure, improve readability, and maintain behavior while making the code easier to maintain.
NEW QUESTION # 73
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application.
How does tokenization affect the cost of using this API?
Answer: B
Explanation:
Tokenization is the process of dividing text into smaller computational units called tokens, which are processed by large language models during inference. Option D is correct because many cloud-hosted LLM APIs calculate usage charges according to the number of input and output tokens processed.
Input tokens represent information transmitted to the model, including prompts, instructions, conversation history, and supporting context. Output tokens represent the content generated by the model.
For example, consider an API charging $2 per million input tokens and $8 per million output tokens. A request containing 2,000 input tokens and generating 1,000 output tokens costs approximately $0.012.
This illustrates why prompt length, context management, and response limits affect operational expenditure.
Option A is incorrect because usage-based token billing is common among commercial LLM APIs, although some services offer subscriptions or provisioned-capacity pricing.
Option B is incorrect because requests with substantially different token counts can incur different charges, even when the API call count remains identical.
Option C is incorrect because tokenization is not itself an additional GPU execution charge. Its cost significance primarily arises from token-based inference billing.
Organizations can optimize expenditure by reducing redundant context, selecting appropriate models, and monitoring token consumption.
Specific Cisco AITECH Course Guide References:
* AI Architect's Toolkit
* Generative AI Ecosystem
* Exam Objective 1.3: Context windows, token limits, and response management
* Exam Objective 5.4: Token usage, prototyping cost, latency, and output quality Cisco explicitly associates token management with AI application economics.
NEW QUESTION # 74
Drag and drop the use case from the left onto the AI system type on the right.
Answer:
Explanation:
Explanation:
Correct Drag-and-Drop Placement:
Generative AI
Drafting a technical summary of a meeting transcript for content creation.
Producing realistic synthetic datasets to validate software edge cases during testing.
Agentic AI
Executing a multi-step sequence to autonomously manage database record deletions.
Observing a failed build, analyzing logs, and deciding which diagnostic tool to run next.
Generative AI and Agentic AI represent different approaches to applying artificial intelligence, particularly regarding content creation, autonomous decision-making, and workflow execution.
1. Generative AI - Technical Summary: Generative AI produces new content based on supplied information.
Summarizing meeting transcripts involves processing existing text, identifying important information, and generating a coherent technical summary. The task does not require autonomous execution of external actions.
2. Generative AI - Synthetic Test Data: Generative AI can create realistic artificial datasets to support software testing. These datasets can simulate normal transactions, boundary conditions, and exceptional scenarios without exposing actual customer information.
3. Agentic AI - Database Record Deletion: Agentic AI can coordinate multiple actions toward a defined objective. Managing database deletions may involve identifying qualifying records, validating deletion criteria, invoking database tools, and verifying results. Production deletions should require appropriate human authorization.
4. Agentic AI - Build Troubleshooting: An agentic system can observe a failed software build, analyze diagnostic logs, select troubleshooting tools, and adapt its next action based on execution results. This demonstrates iterative, goal-directed problem-solving.
The fundamental distinction is that Generative AI primarily creates content, whereas Agentic AI coordinates decisions and actions to accomplish operational goals.
Specific Cisco AITECH Course Guide References:
* Generative AI Ecosystem
* AI-Driven Software Engineering
* AI-Powered Workflows and Agentic AI
* Exam Objective 6.1: Differentiate Agentic AI from Generative AI use cases
* Exam Objective 6.2: AI agent design principles, autonomous capabilities, and orchestration
* Exam Objective 5.1: AI across the software development lifecycle
Cisco's published AITECH blueprint explicitly covers these distinctions.
NEW QUESTION # 75
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